Algorithm Research Of Target Parameter Estimation And Beampattern Optimization For MIMO Radar

Compared with the traditional phased array radar,multiple-input multiple-output(MIMO)radar has spatial and waveform diversity characteristics,which can significantly improve the performance of target detection,parameter estimation and target tracking.However,as the application scenarios of MIMO radar expand,the classical parameter estimation and signal processing algorithms can no longer meet the practical application requirements.This dissertation addresses the problems of target parameter estimation in the near-field,mixed far-field and near-field,and far-field background of MIMO radar and the joint optimization of the transmit-receive beamforming for interference suppression problem of MIMO radar.The main contributions and innovations of this dissertation can be summarized as follows:1.For the parameter estimation problem of near-field multi-targets with sparse transmit arrays and compact receive array for MIMO radar,two algorithms for estimating the parameters of MIMO radar near-field targets based on sparse recovery are proposed.The proposed algorithms decompose the two-dimensional parameters estimation problem of angle-range into multiple one-dimensional parameter estimation problems to reduce the computational complexity;Based on the principle of compressed sensing,corresponding optimization problem models are established for the target angle estimation and range estimation,respectively,where the first problem model exploits the orthogonality property of the signal and noise subspaces,and the second problem model jointly exploits the sparsity property of the signal and the noise covariance matrix.The proposed algorithm can realize the automatic pairing of angle and range,and improve the estimation accuracy of near-field target parameters and target classification performance.2.To address the problem of parameter estimation of mixed far-field and near-field multi-targets for the MIMO radar with sparse transmit array and compact receive array,an algorithm for the mixed far-field and near-field target classification and parameter estimation based on fourth-order cumulant matrix differencing is proposed.The proposed algorithm separates the far-field target component and the near-field target component based on the matrix self-differencing and matrix mutual-differencing methods to solve the spurious peak problem existing in the traditional mixed far-field and near-field target parameter estimation algorithm.The proposed component separation method based on the persymmetric property of the array far-field steering vectors is more general than the currently used component separation method based on the Toeplitz property of the far-field target covariance matrix.3.To deal with the problem of the sum-difference beam monopulse angle estimation of the compact transceiver MIMO radar,the performance of the adaptive sum-difference beam monopulse angle estimation of MIMO radar is analyzed in detail,including the distribution characteristics of the monopulse ratio estimation error in the background of Gaussian interference and noise,the Cramér-Rao Bound(CRB)expression of monopulse ratio estimation,and the angle conversion error of monopulse ratio.Further,an improved adaptive sum-difference beam monopulse angle estimation method is proposed to balance the effects of monopulse ratio estimation error and monopulse ratio angle conversion error on the angle estimation accuracy.4.A one-step iterative optimization algorithm with low complexity is proposed based on the convex approximation method for the joint transmit-receive beamforming optimization design problem of the compact transceiver MIMO radar.The proposed algorithm models the optimization problem by adopting the signal-to-interference-plus-noise ratio(SINR)criterion for maximizing the minimum output signal-to-noise ratio(SINR)in multi-target.The binary parameter optimization problem is transformed into a oneparameter optimization problem by analyzing the intrinsic connection between receiving and transmitting weights in order to reduce the computational complexity.The proposed algorithm can reasonably allocate the transmitted energy for simultaneous multi-target detection to improve the detection efficiency and output SINR performance.